Q1 = read_csv(here("/courses","EDS214", "ShaleCoRyJoe","data", "QuebradaCuenca1-Bisley.csv"), na = "-9999")
Q2 = read_csv(here("/courses","EDS214", "ShaleCoRyJoe","data", "QuebradaCuenca2-Bisley.csv"), na = "-9999")
Q3 = read_csv(here("/courses","EDS214", "ShaleCoRyJoe","data", "QuebradaCuenca3-Bisley.csv"), na = "-9999")
PRM = read_csv(here("/courses","EDS214", "ShaleCoRyJoe","data", "RioMameyesPuenteRoto.csv"), na = "-9999")
PRM_temp <- PRM %>%
count(Temp == "NA")
Q1_temp <- Q1 %>%
count(Temp == "NA")
Q2_temp <- Q2 %>%
count(Temp == "NA")
Q3_temp <- Q3 %>%
count(Temp == "NA")
# It seems that 3/4 of the data values have a value for temperature, we consider that enough to continue exploration
Q1.2 = Q1 %>%
clean_names() %>%
select(sample_id, sample_date, temp) %>%
mutate(sample_date = mdy(sample_date)) %>%
group_by(sample_id, sample_date) %>%
summarize(temp_daily_mean = mean(temp))
Q2.2 = Q2 %>%
clean_names() %>%
select(sample_id, sample_date, temp) %>%
mutate(sample_date = mdy_hm(sample_date)) %>%
group_by(sample_id, sample_date) %>%
summarize(temp_daily_mean = mean(temp))
Q3.2 = Q3 %>%
clean_names() %>%
select(sample_id, sample_date, temp) %>%
mutate(sample_date = mdy(sample_date)) %>%
group_by(sample_id, sample_date) %>%
summarize(temp_daily_mean = mean(temp))
PRM.2 = PRM %>%
clean_names() %>%
select(sample_id, sample_date, temp) %>%
mutate(sample_date = mdy(sample_date)) %>%
mutate(sample_id = str_replace(string = sample_id, pattern = "MPR", replacement = "PRM")) %>%
group_by(sample_id, sample_date) %>%
summarize(temp_daily_mean = mean(temp))
combined_data_temp <- rbind(Q1.2, Q2.2, Q3.2, PRM.2)
# eliminate 1 major outlier that was recorded incorrectly (>1000 degrees celcius)
combined_data_temp <- combined_data_temp %>%
filter(temp_daily_mean < 1000, temp_daily_mean > 10)
write_csv(combined_data_temp, "combined_data_temp.csv")
combined_data_temp_plot <- ggplot(data = combined_data_temp, aes(x = sample_date, y = temp_daily_mean, color = sample_id), na.rm = TRUE) +
geom_line() +
facet_wrap(vars(sample_id))
combined_data_temp_plot
plotly_plot <- ggplotly(combined_data_temp_plot)
plotly_plot
Climate change temperature plotly 2000 - 2012
combined_data_temp_climate <- combined_data_temp %>%
filter(sample_date >= as.Date("2000-01-01"))
combined_data_temp_climate_plot <- ggplot(data = combined_data_temp_climate,
aes(x = sample_date,
y = temp_daily_mean,
color = sample_id)) +
geom_line() +
facet_wrap(vars(sample_id)) +
labs(
title = "Luquillo Watershed Temperature Data 2000 - 2012",
x = "Temperature (c)",
y = "Date"
) +
theme(plot.title = element_text(hjust = 0.5))
combined_data_temp_climate_plot
combined_data_temp_climate_plotly <- ggplotly(combined_data_temp_climate_plot)
combined_data_temp_climate_plotly